1. Technical Field
The field of the invention relates to a strategic, planning and controlling method for industry and a data collecting and tracking means thereof and more specifically to a method for analyzing an organization, its process drivers, its resources, its input and output entities and the risks and opportunities of a plurality of on-going projects of the organization, so that its processes can be controlled to achieve the desired goals of the organization.
2. Background Art
Conventional costing and management decision support systems use traditional profit and loss statements to analyze costs such as salaries, equipment, facilities, and administrative expenses. Based on these figures, business managers use direct material and labor consumption as the primary means of determining product costs and sale prices, and apportioning overhead costs. This method has been adequate when the overhead and administrative cost of activities, not directly related to production, was small compared with the direct material and labor required to manufacture the end product. However, in today's service businesses and manufacturing environments, automation has substantially reduced the amount of direct material and labor consumption, so that indirect activities have become a significant factor contributing to the cost of making the product. The result of conventional systems can give business managers an inaccurate view of how the business organization spends money, which may cause them to make pricing errors, mis-allocate resources, and make strategic mistakes.
In today's economy, information and intangible assets are of greater significance to businesses than in the past when physical assets were the almost the total measure of a business. Therefore new methods need to be developed so the effect of information and intangible assets on a business can be measured just as physical assets were measured.
The popular prior art way of viewing an organization's operations is to associate costs with activities. Activity-based costing measures the cost and performance of activities and products. In product costing applications, for example, activity-based costing allows costs to be apportioned to products by the activities and resources consumed in procuring parts or materials, manufacturing, marketing, selling, delivering, and servicing the product. With activity-based costing information, managers can be provided with a different gauge of the business operations.
An example of an activity-based management system is found in U.S. Pat. No. 5,799,286 (1998) to Morgan et al. The focus of the system is defining the operation of an organization in terms of the activities it performs. In an activity-based system, the production activities are isolated and information is acquired which is relevant mainly to the specific activities.
For example U.S. Pat. No. 5,799,286 describes an automated activity-based management system for a business organization occupying facilities, employing people and using equipment to produce products and provide services is provided. The system includes a database which receives traditional accounting information and accepts information related to activities provided by the users. The activity information includes the activities performed, the percentage of time each activity is performed, equipment utilization data, and space utilization data. Further included is a people module for processing the traditional accounting information and activity information to generate a people cost component associated with each activity. A facilities module processes the traditional accounting information and activity information and generates a facilities cost component associated with each activity. An equipment module processes the traditional accounting information and activity information and generates an equipment cost component associated with each activity. An overhead module is also provided to process the traditional accounting information and activity information to generate an overhead cost component associated with each activity. A reporting module generates cost summaries of the activities.
Based on the above information, a people cost component, a facilities cost component, an equipment cost component, and an overhead cost component associated with each activity are computed. Based on the activity costs and the output resulting from the activities, the value of activities performed by an organization are determined. According to the activity costs, the activities can be prioritized to emphasize valuable activities and de-emphasize or eliminate wasteful or unnecessary activities. Resources such as facilities and equipment can also be utilized.
Other prior art management methods include methods for planning business systems having specific work, project or scheduling patterns, like production and manufacturing or project management. For example there are many prior art methods for optimizing the manufacturing method in a factory. In order to configure a production plan which yields the best performance, the capacity, or the amount of work the facility can handle, must be modeled in some fashion, since starting work above the capacity of the facility compromises performance and brings forth no benefits. Conventional factory capacity models employ simple steady-state linear relations that include: (1) the average amount of available work time for each machine in the factory and (2) the amount of work each product requires of each machine. From the above linear relations, a given start plan is within capacity if, for each machine, the total required amount of work is: (1) less than the machine's available time, and (2) multiplied by a predetermined fraction goal utilization of the start rate.
U.S. Pat. No. 5,586,021 (1996) to Farger et al. is a modification of the conventional capacity model above. In U.S. Pat. No. 5,586,021 the production plan is represented by the processing capacity of each resource group in the factory, divided into contiguous time intervals, together with the work planned for each time interval. Work is represented within time intervals by the total processing committed by each resource group. No sequencing of work is performed within a time interval. This may be referred to as a ‘time-phased’ model.
Each job to be planned, which may have an estimated total cycle time of many time intervals, is represented by first dividing the required processing into discrete segments, where each segment represents processing on resources which may be completed within one time interval of the plan representation. Division of processing into segments is performed by calculating which segment each processing step would lie in if processing were evenly distributed over the entire cycle time. This model is still an activity based model because it focuses on the individual jobs or activities to be performed.
For project planning, U.S. Pat. No. 5,826,252 (1998) to Wolters, Jr. et al. describes a system for managing multiple projects of a similar type. It has a global project management database for storing data for all participating projects which is dynamically updated with best current data representing best current practices across all participating projects in the system. Localized computer terminals are operated at each local site with a common project management program and data imported from the global project management database. Periodically, the local terminals export data to the global project management database which are evaluated to determine any new best current practices across all participating projects and to update the global project management database with the new best current practices. Upon periodically importing data from the global project management database, each localized computer terminal is updated with the new best current practices across all participating projects.
Finally, U.S. Pat. No. 5,255,181 (1993) to Chapman et al. describes a method of planning organizational activities based on a prioritizing system using a simulator. The method maintains a time-valued list of existing commitments to resources. Allocations of these resources are made to lots during a simulation procedure which calculates a resulting plan's timing data. The method simulates higher priority lots before it simulates lower priority lots. A simulation evaluates the process flow description to obtain the relative order of consuming and releasing resources, resource attributes and related capabilities, initial minimum timing requests, and process control rules. The simulation uses the list to determine when resources may be used without impacting prior commitments of the resources. In addition, the simulation forces the allocations to conform to the process control rules. The resulting timing data is merged into the processing plan, and resource commitments are then made to the simulated resource. When lower priority lots are simulated, commitments have already been made to higher priority lots. Thus, the lower priority lots cannot receive resource allocations which impact the higher priority lots.
As shown above the prior art methods focus on small snap shots of an organization such as an activity (project) or activities or resource allocation among activities. Optimizing particular activities may be beneficial, but it does not necessarily result in the optimization of the entire organization. Without an accurate model, an organization is still a black box.
Other solutions for management decision support are currently known as project management, business intelligence and data mining.
Project management is a term used to describe the process of designing and monitoring a project based on a work breakdown structure (WBS) and task assignments for staffing resources over time. The project manager determines the deliverables or goals for the project. These goals are broken down into tasks and possibly sub-tasks. Each task maybe dependent or non-dependent on any number of other tasks to be completed. The division of a large project into a plurality of groups of dependent tasks and non-dependent tasks is called the WBS. Next, each task is assigned to a selection of staffing resource(s). This assignment ensures work balancing (avg. of 40 hrs per week per person) through average allocation of work load (tasks) to each staffing resource. As work progresses, each task in the WBS can be manually monitored as to percent complete.
The goals of Project Management is simply, (1) breakdown of one project in to all individual smaller tasks associated with the project, assignment of individual tasks to different staffing resources, balancing of the work load or tasks (for example, avg. 40 hrs per week per staffing resources) across the staffing resources and tracking of the percentage completion of individual tasks and the entire project.
Business Intelligence (BI) is a term used to describe the collection of information from existing databases. For example, revenue information is usually at the transaction level (i.e. sales for a particular product from one store). The consolidation of this information over a company's disparate databases gives complete transactional view (i.e. total sales for all products across the United States). BI alone does not refer to the mapping of which information is to be collected. Consultants assist in determining what information needs to be collected, then use BI tools to create a collection system.
Data Mining is a term used to describe an analysis technique for pattern recognition. Data mining evaluates a selection of data in a spreadsheet or database for correlations. An analyst will look for certain types of information, when combined in an equation, serve as a “predictor” to the intended outcome. Data mining tools select one attribute or column of a data set as the “response” or goal. Next, any or all other attributes may be selected to as predictors to the response. The tool then evaluates the complete data set, including all samples in the data set (the rows), to determine which attributes combine to create a predictive equation for the response.
Prior Art System Model
FIG. 1 shows a schematic diagram of system 1, as typically modeled in systems engineering, having the typical elements of a system, namely: activities 3, resources 5, controls 7, input entity 9 and output entity 11. Because systems are typically modeled in terms of inputs and outputs from a system, the unidirectional arrows between the elements in system 1 indicate the direction of input and output from system 1.
The smaller squares within each element of system 1 indicates a sub-element, such as activities 3a–c, resources 5a–c, controls 7a–c, input entity 9a–c and output entity 11a–c. For simplicity only one level of sub-elements are shown in FIG. 1. However, in a typical system, each activity, for example, may be broken down into sub-activities 3a–c, each one of which may be broken down into additional sub-activities, and further each one of the sub-activities may be broken down into more sub-activities to an infinite level of sub-activities.
FIG. 2 is a schematic drawing showing system 1 modeling an actual working example. The system 1 of FIG. 2 has the same elements as described in FIG. 1. Activities 3 is described in terms of a process having six sub-activities 3a–f. Resources 5 contain four sub-resources 5a–d and controls 7 contain two sub-controls 7a–b. From the system 1 in FIG. 2, two input entities 9a–b are input into activities 3 and are processed and subsequently output from activities 3 in three output entities 11a–c. 
Schematically shown in all of the sub-elements of FIG. 2 are three triangles with one inside of another. The triangles represent the prior art manner in which system 1 is measured and understood. Today, systems, including complex business systems are modeled in terms of inputs and outputs from a system. Within the system are certain known activities which are performed and which consume resources and are governed by controls. In order to measure and understand a system, each activity, resource, control and entity associated with the system is broken down into smaller and smaller sub-parts so that very detailed information about the small sub-elements which comprise the system can be obtained. The logic behind such a measurement method is simply the paradigm that if more detailed information is collected about the system it will be understood in terms of that level of detail. Computers have significantly aided in the collection and storage of very detailed information about systems.
The danger in measuring and understanding a system in terms of detailed information collected about the system is that such methodology overlooks important relationships about the system which occur at a higher or more general level. In other words, critical relationships, such as scheduling among the activities, which take place among the activities would be overlooked because the relationship occurs at the most general level rather than at the level of minute detail. In fact, by studying minute detail, one is simply studying relationships and patterns which may have no overall affect on the entire system. Simply because there is error and variation in modeling and studying systems, studying relationships between or among minute detail may become irrelevant if the standard deviation of the measurement of the sub-elements is larger than the level of detail of information which is collected.
FIG. 3 is a second example of system 1 having the typical elements of a system, namely: activities 3a, 3b and 3c; resources 5; controls 7; and input entity 9. As shown by the squares or triangles within each of the elements, there is a sub-element of that particular element, such as activities 3aa and 3ab. This shows that activities, namely activities 3a and 3b, can affect other activities such as activity 3a. 
In fact, the system 1 of FIG. 3 is a model of a human system, namely energy consumption and how the system of energy consumption would be studied by conventional methods. As previously mentioned, systems are modeled in terms of inputs and outputs, resources, activities and controls which are relevant to the system. Systems are understood by breaking down the systems into as many minute sub-elements as possible and collecting information on all sub-elements. As previously explained, it is thought that by collecting detailed information the system as a whole will be understood on the detailed information level.
Referring to the system of energy consumption, energy consumption is itself an activity, indicated in FIG. 3 as activity 3a. If an individual were to study his or her own energy consumption it would most likely be for the purpose of gaining a better understanding of energy consumption to improve energy consumption in the body. In the example of FIG. 3 the goal of understanding energy consumption will be to improve energy consumption.
In the system 1 of FIG. 3 the sub-element squares indicate the level of the sub-element. For every element there is one level of sub-elements, such as for activity 3a there are two sub-elements 3aa and 3ab at the first level. Sub-elements at a more detailed level are shown by a square within a square. For the purposes of measuring and understanding energy consumption, it is practical to model the system at the first level of sub-elements. This is because in this example the goal is to improve energy consumption as it relates to personal appearance. If the goal were to study energy consumption at the cellular level, then energy consumption would likely be modeled with more sub-elements to obtain information about energy consumption at the cellular level.
In the example in FIG. 3, energy consumption 3a may be thought of in terms of energy consumption while awake 3aa and energy consumption during sleep 3ab. Input into the activity consumption 3a are resources 5 which can be fat 5a and genetics 5b. Input into activity 3a, specifically energy consumption while awake 3aa, is another activity namely, exercise 3b. Exercise 3b may be thought of in terms of types of exercises 3ba–bd. Another activity which is input into energy consumption 3a, specifically energy consumption 3aa, is normal movement or normal activity 3c. 
Controls 7 which are actively studied in a typical human energy consumption system are a diet schedule 7a and an exercise schedule 7b. Input into system 1 is the input entity food 9 which can be thought of in terms of three meals 9a–c. Input entity food 9 will result in an output entity (not shown) that is not important in this particular system 1.
It is easy to understand the measurement methodology system 1 of FIG. 3 namely the energy consumption in the human body. For example, diet schedule 7a can be measured in terms of calories. Exercise schedule 7b can be measured in terms of time. The activity energy consumption 3a itself may be measured in terms of weight, namely as energy consumption increases more of the resource fat 5a is consumed, if input entity food 9 remains constant. The activity exercise can be measured in terms of time or distance in the performance of exercise 3ba–bd. The activity of normal movement 3c can be measured in terms of time walking or time each day spent awake. Input entity food 9 can be measured in terms of calories or food groups for each meal 9a–c. 
Thus the system 1 of FIG. 3 illustrates the common elements associated with energy consumption 3a and the common methods of measuring the elements. Each of the elements is broken down into sub-elements of which more detailed information is collected, as represented by the triangles within each other. In this system 1, input entity 9, resources 5, activity 3b, activity 3c and activity 3a can be measured in terms of calories simply because each elements can be measured in terms of a caloric unit. A caloric unit of measurement is a very detailed level of information about a particular element such as activity exercise 3b. Activity exercise 3b can be measured or understood at a more general level than the caloric level, such as different types of activity exercises 3b, which may be more effective information for a person trying to understand his or her personal energy consumption.
Knowledge of the system in terms of calories may not be information which is relevant toward improving personal energy consumption. Certainly, calorie consumption versus time gives one historical information about energy consumption over a period of time. However, to specifically improve energy consumption, time and calories does not necessary provide information for directly affecting energy consumption by improving energy consumption in one's daily life.
In other words, by equating resources, activities, and entities in terms of calories, one may only learn differences among the elements. As shown in FIG. 4 measuring the elements involved in the energy consumption system gives one a view such as that diagramed in FIG. 4. FIG. 4 shows energy consumption activity 3a being related to input entity 9, resources 5, activity 3b, activity 3c and control 7. If the elements were measured in terms of calories, FIG. 4 would show the differences among the elements in terms of calories. To obtain information relevant to the controls, specifically diet schedule 7a, the information would need to correlated in some manner, for example in a manner as relevant to time. As shown in FIG. 4, if the elements were defined in terms of calories, mere differences among the elements do not indicate a proportional relationship among the elements, in terms of the direct effect of each element on activity 3a. While more energy may be consumed by exercise than by normal movement, normal movement is fundamental to the human system. Therefore, one would not necessarily decide to decrease the amount of normal movement because less energy is consumed in terms of calories.
The problem with measuring the typical system shown in FIGS. 3 and 4 is that lots of detailed information can be collected and correlated, however, such information does not necessarily assist in making a decision about improving energy consumption. Having information about elements in terms of total calories, one could certainly choose among elements which theoretically consume more energy, but if those elements are activities which are never performed then the goal of improving energy consumption is not realized. Therefore, knowing the differences in energy consumption among the elements in terms of calories first, is not the most accurate system model for energy consumption because such a system is affected things other than calories, like the scheduling of the activities 3a–c, and second, because it is not an accurate system model it does not give one useful information for making decisions that directly impact energy consumption.
The prior art model described above is like an activity-based costing model except instead of measuring the activities in terms of dollars they are measured in terms of calories.